Gastos Casa
10 November, 2025
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 10/9/2025 | Diosi | 49990 | Andrés | braloy 7.5 kg |
| 13/9/2025 | Comida | 60499 | Tami | Supermercado |
| 23/9/2025 | Comida | 76691 | Tami | Supermercado |
| 28/9/2025 | Comida | 87200 | Tami | Supermercado |
| 30/9/2025 | Gas | 82000 | Andrés | 79500+propina |
| 24/9/2025 | Electrodomésticos/mantención casa | 40000 | Andrés | mantención calefont 50 mil |
| 23/9/2025 | Gas | 80000 | Andrés | gas 79 mil + propina |
| 30/9/2025 | VTR | 22000 | Andrés | NA |
| 4/10/2025 | Comida | 67440 | Tami | Supermercado |
| 5/10/2025 | Comida | 7190 | Andrés | NA |
| 6/10/2025 | Electricidad | 75365 | Andrés | del mes pasado |
| 6/10/2025 | Comida | 7660 | Andrés | NA |
| 10/10/2025 | Enceres | 16990 | Andrés | casa nativa |
| 12/10/2025 | Comida | 99947 | Tami | Supermercado |
| 18/10/2025 | Comida | 114896 | Tami | Supermercado |
| 22/10/2025 | Otros | 18275 | Andrés | alfred anual |
| 25/10/2025 | Comida | 87042 | Tami | Supermercado |
| 25/10/2025 | Comida | 37026 | Andrés | la burguesia |
| 27/10/2025 | Electricidad | 48758 | Andrés | de este mes |
| 28/10/2025 | Comida | 5970 | Andrés | lider de 27-10-2025 |
| 30/10/2025 | Agua | 37400 | Andrés | los ultimos 2 mese que no me han cobrado mas 17 lks por lo que habriamos gastado en junio |
| 1/11/2025 | Comida | 112523 | Tami | Supermercado |
| 1/11/2025 | Diosi | 27990 | Andrés | arena american litter |
| 3/11/2025 | Diosi | 29568 | Andrés | arena diosi 2x 20kg |
| 3/11/2025 | Comida | 44434 | Andrés | bolsas caca diosi + soul bar 2x20un |
| 8/11/2025 | Diosi | 65000 | Tami | Veterinaria |
| 8/11/2025 | Comida | 101773 | Tami | Supermercado |
| 8/11/2025 | Comida | 20000 | Andrés | lo saldes tami |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 1.0786e+09 2 5.5924 0.0039 **
## lag_depvar 2.6719e+11 1 2770.7995 <2e-16 ***
## Residuals 8.4378e+10 875
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1708.689 16166.37 0.1395881
## 2-0 31725.608 23694.094 39757.12 0.0000000
## 2-1 24496.770 19860.690 29132.85 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
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## 845 48788.71 2 34774.86
## 846 50717.71 2 48788.71
## 847 51727.43 2 50717.71
## 848 51313.14 2 51727.43
## 849 56125.29 2 51313.14
## 850 68503.71 2 56125.29
## 851 62945.00 2 68503.71
## 852 50209.71 2 62945.00
## 853 49436.29 2 50209.71
## 854 55308.00 2 49436.29
## 855 62650.86 2 55308.00
## 856 55683.00 2 62650.86
## 857 49762.14 2 55683.00
## 858 51835.00 2 49762.14
## 859 49806.43 2 51835.00
## 860 52799.57 2 49806.43
## 861 50620.71 2 52799.57
## 862 49192.00 2 50620.71
## 863 66245.86 2 49192.00
## 864 62359.71 2 66245.86
## 865 70816.86 2 62359.71
## 866 84559.71 2 70816.86
## 867 80831.43 2 84559.71
## 868 74717.14 2 80831.43
## 869 77935.14 2 74717.14
## 870 57977.86 2 77935.14
## 871 66541.71 2 57977.86
## 872 70498.29 2 66541.71
## 873 58251.86 2 70498.29
## 874 66341.57 2 58251.86
## 875 72279.86 2 66341.57
## 876 61272.00 2 72279.86
## 877 64187.71 2 61272.00
## 878 69982.86 2 64187.71
## 879 64140.86 2 69982.86
## 880 88212.00 2 64140.86
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 723 53959.87 21704.767
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86 59799.14 53577.14 61775.29 70627.86
## [806] 57888.43 49960.71 42923.71 47284.86 52284.86 50191.00 36465.86
## [813] 34525.14 43199.14 52757.43 43200.86 36772.29 29568.00 42362.00
## [820] 42566.29 39596.00 32925.00 43416.57 52624.86 57733.71 54120.57
## [827] 53353.43 56286.86 60626.86 61375.29 53710.86 55795.57 55130.14
## [834] 57700.14 61333.14 59230.71 49195.00 55436.43 50353.14 43194.86
## [841] 47539.71 35271.00 34774.86 48788.71 50717.71 51727.43 51313.14
## [848] 56125.29 68503.71 62945.00 50209.71 49436.29 55308.00 62650.86
## [855] 55683.00 49762.14 51835.00 49806.43 52799.57 50620.71 49192.00
## [862] 66245.86 62359.71 70816.86 84559.71 80831.43 74717.14 77935.14
## [869] 57977.86 66541.71 70498.29 58251.86 66341.57 72279.86 61272.00
## [876] 64187.71 69982.86 64140.86 88212.00
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [852] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2015.326468 4038.846593 -536.522780 2439.414489 -2966.568519
## 7 8 9 10 11
## 519.859444 -5654.211320 -1189.545882 -3968.053655 -421.730523
## 12 13 14 15 16
## -4942.929279 -1614.883533 -905.174302 372.544707 -3246.588052
## 17 18 19 20 21
## -382.761462 -2134.214895 6599.606575 -1528.958595 -1208.714795
## 22 23 24 25 26
## 1474.818819 -1186.106568 234.673605 1695.493461 -7100.251964
## 27 28 29 30 31
## 945.258643 8191.417039 422.804473 -9.259062 -2396.000966
## 32 33 34 35 36
## 1579.197681 4576.531611 1133.681579 2398.519633 -1859.882992
## 37 38 39 40 41
## 4614.364325 4307.553858 -2269.420206 -2977.258303 -1108.246391
## 42 43 44 45 46
## -10740.420369 7283.783435 2556.803041 1368.021578 8107.051956
## 47 48 49 50 51
## 692.749253 6534.990820 6723.846626 -5869.950963 -4788.109740
## 52 53 54 55 56
## -5056.161319 -7928.449411 6125.175575 -4076.935003 -4897.043203
## 57 58 59 60 61
## 3850.498161 885.526081 -33.585804 140.935377 -4997.457222
## 62 63 64 65 66
## 18122.893539 3648.229426 -3637.234269 5931.012378 7352.674133
## 67 68 69 70 71
## 14651.017792 1712.839163 -13194.338140 -1297.799503 4650.188352
## 72 73 74 75 76
## -4891.522325 -4399.644960 -10495.611722 2462.571478 -5401.790784
## 77 78 79 80 81
## 1059.080001 -6869.543499 540.289682 -2359.867607 -2699.660883
## 82 83 84 85 86
## -3938.297238 -545.185913 2307.774269 3758.116093 475.073402
## 87 88 89 90 91
## -485.880135 195.169665 4300.793059 -1161.565670 1151.475990
## 92 93 94 95 96
## -2063.244337 -1044.356262 176.975748 274.388857 -7484.192772
## 97 98 99 100 101
## 2387.546230 -8604.723745 -2947.058235 -4046.447673 -1744.714050
## 102 103 104 105 106
## -1268.937391 3173.914159 -2346.304689 2589.152400 -1161.145674
## 107 108 109 110 111
## 967.524552 2585.136672 -3154.623477 -4724.901059 -854.336477
## 112 113 114 115 116
## 1899.623772 11691.278728 -1238.133232 2671.814631 4267.266560
## 117 118 119 120 121
## 3508.998933 -1092.366413 -4710.174990 -3721.151174 2320.459730
## 122 123 124 125 126
## -1730.634530 1341.379990 8859.951768 853.579400 136.705313
## 127 128 129 130 131
## -2515.595707 2658.771281 7057.343056 1020.648835 -8491.557197
## 132 133 134 135 136
## 1751.495243 4138.572387 -3158.920423 -1416.974897 -852.246242
## 137 138 139 140 141
## -3878.857593 1182.040718 -495.601449 -2913.882524 1716.429665
## 142 143 144 145 146
## -1881.566839 -7830.667915 2034.084673 -3483.225855 2097.312883
## 147 148 149 150 151
## -260.604216 1020.169617 -361.228608 1350.331105 1185.756544
## 152 153 154 155 156
## 3356.476828 -4860.026892 -1175.529228 -3237.321226 5953.693803
## 157 158 159 160 161
## 9747.275304 -3747.694551 -5094.954686 3286.019910 -120.034915
## 162 163 164 165 166
## 2382.186215 -6222.783986 -7062.330502 3839.234831 17077.505855
## 167 168 169 170 171
## 3313.443141 -713.321731 -2761.700255 -1420.962284 3272.933132
## 172 173 174 175 176
## -545.817472 -8393.693190 2543.570690 4005.364934 305.510890
## 177 178 179 180 181
## 8429.904527 -9568.657550 -3793.643340 -11067.322374 -11564.429022
## 182 183 184 185 186
## 909.190058 8967.904114 -1754.651471 5603.423871 6229.229542
## 187 188 189 190 191
## 12829.888493 8098.206547 -4400.610105 2123.862953 10024.070325
## 192 193 194 195 196
## -1992.983531 -2796.127848 -10633.417965 -6714.986056 883.156302
## 197 198 199 200 201
## -5583.484051 -10144.345032 5038.601411 -3412.972643 -2055.437951
## 202 203 204 205 206
## -1145.963860 6153.151256 9537.296122 227.282094 2571.971646
## 207 208 209 210 211
## 2743.216588 5427.877408 12474.638080 -6051.474094 -11657.431351
## 212 213 214 215 216
## -6022.175384 -10939.646289 -5421.807018 1183.658676 -13353.021736
## 217 218 219 220 221
## 16053.191318 7466.092953 1180.378117 26338.945466 12161.456817
## 222 223 224 225 226
## 6962.303864 13649.800745 -4297.517125 -2121.839918 3398.989196
## 227 228 229 230 231
## -17.952083 2371.006731 8631.780157 5458.195825 -2275.439295
## 232 233 234 235 236
## -2192.773912 9064.407000 -11871.759171 -7640.399731 -8894.074366
## 237 238 239 240 241
## -10450.063167 2733.896510 1007.795994 -8641.301686 -9329.511843
## 242 243 244 245 246
## 8759.198944 -8105.249581 2151.057426 -10638.929279 -4387.043100
## 247 248 249 250 251
## 1090.910527 669.525043 -12651.054965 3313.576398 1729.245545
## 252 253 254 255 256
## 3875.776635 1794.605620 -1503.577896 10795.373973 20527.605157
## 257 258 259 260 261
## 2825.051578 -4647.496006 3735.693035 -2071.743246 3361.519986
## 262 263 264 265 266
## -5229.808937 -11267.335136 -5093.431011 -882.518009 -5548.564480
## 267 268 269 270 271
## 8421.578312 -4645.641246 3826.779886 -2474.446481 4064.295674
## 272 273 274 275 276
## 337.268089 6929.644421 -1793.742356 11644.019573 -4979.859333
## 277 278 279 280 281
## 1333.572873 -766.240210 7458.090825 -5459.558029 -3125.745652
## 282 283 284 285 286
## -11650.565788 -3041.946934 18286.872074 7391.307734 2331.930771
## 287 288 289 290 291
## -1033.520131 503.238113 5995.542207 6472.167870 -19190.369630
## 292 293 294 295 296
## -11524.214288 -8485.552178 9316.391370 2709.734818 -1545.207732
## 297 298 299 300 301
## 27038.667254 9656.926448 4477.949572 9090.531699 2417.591727
## 302 303 304 305 306
## -1470.627527 7465.903141 -24733.679755 -3921.512274 -550.543618
## 307 308 309 310 311
## -7339.003969 -4325.822572 2588.591775 -9539.183005 -3556.900218
## 312 313 314 315 316
## -8505.021364 1263.788362 -3456.553448 1747.813139 -4389.577906
## 317 318 319 320 321
## 27143.704134 -1104.756825 2911.882225 10444.585947 5182.151231
## 322 323 324 325 326
## 31965.378807 4635.577906 -21410.659040 1374.473591 695.771443
## 327 328 329 330 331
## -6875.273946 -2124.117898 -33648.502599 613.822932 -2569.301633
## 332 333 334 335 336
## -352.960385 -3426.652981 3833.996109 -699.711582 -7215.226202
## 337 338 339 340 341
## -3363.744175 -2433.436603 -7918.630301 3628.065580 -1609.411371
## 342 343 344 345 346
## -1976.730566 -1232.518467 -63.592443 236.810222 -1868.840154
## 347 348 349 350 351
## -9697.157084 -13441.662883 2105.553230 -4541.139920 -3871.823219
## 352 353 354 355 356
## -6190.587599 1548.299438 1168.724324 2525.429400 -4009.807375
## 357 358 359 360 361
## -756.706517 431.433092 6759.414170 -3.044624 -322.588912
## 362 363 364 365 366
## 2295.527751 -3047.664712 -1167.793105 -9031.954957 -4888.629774
## 367 368 369 370 371
## -6462.776538 -5183.886667 -7476.175936 4807.565429 140.106785
## 372 373 374 375 376
## 6880.919882 -7902.971322 -2509.481705 -3631.263328 -2703.913951
## 377 378 379 380 381
## -12691.131905 1701.860867 -10848.857491 5506.296529 9118.553532
## 382 383 384 385 386
## 2870.492311 -2670.340058 1335.714836 6465.260238 11105.742614
## 387 388 389 390 391
## -6148.009786 -5694.126587 -474.735502 8244.330324 1466.322549
## 392 393 394 395 396
## 10866.688266 -10270.568812 2413.908140 343.963102 192.853249
## 397 398 399 400 401
## -1023.319455 -928.427814 -14848.684563 8216.966418 -1510.188385
## 402 403 404 405 406
## -1694.361412 6667.075906 -8269.294462 -1602.745880 -2829.691765
## 407 408 409 410 411
## -6105.476979 -3122.673622 -4170.185284 -8995.530597 5921.006856
## 412 413 414 415 416
## 1400.715503 -7623.695545 -7920.558066 14014.355745 3548.832894
## 417 418 419 420 421
## 4203.397312 -8345.780414 -5026.306813 -2866.482263 2562.840177
## 422 423 424 425 426
## -14280.075112 -3012.807670 -9314.526562 2824.035745 6769.224977
## 427 428 429 430 431
## 6334.216215 -4258.638889 -4380.807147 -4970.406058 -2024.541315
## 432 433 434 435 436
## -5944.907272 -6844.524465 -6150.533211 -1581.141489 -1039.603815
## 437 438 439 440 441
## -5172.699411 2392.783431 4632.118023 -5289.357056 -2379.283601
## 442 443 444 445 446
## 1356.344051 -4069.157142 2611.267726 -6818.307592 -12332.809451
## 447 448 449 450 451
## -4698.685180 9464.780890 -2253.254679 4534.538455 -6110.649807
## 452 453 454 455 456
## -1347.821476 157.769068 2794.323956 -12514.614108 3159.485196
## 457 458 459 460 461
## -6927.767398 6312.600432 2775.542559 2256.566232 -4107.204856
## 462 463 464 465 466
## 1841.983748 -267.381303 1531.145825 -790.511091 3081.736452
## 467 468 469 470 471
## -2919.711165 5533.779263 -7231.891884 -3228.840646 -2459.054100
## 472 473 474 475 476
## -4910.053793 2764.585621 7553.580395 -6287.527664 1233.709326
## 477 478 479 480 481
## -6434.731416 -3080.843767 1782.814635 -13167.854091 -9955.940691
## 482 483 484 485 486
## -1379.412630 -161.039815 -1151.194730 -1535.007435 -9780.580207
## 487 488 489 490 491
## 10921.302395 6019.375991 7180.451110 -5702.080819 5117.723882
## 492 493 494 495 496
## 9025.117489 5757.010024 -13786.974182 -10833.826156 -3675.501585
## 497 498 499 500 501
## -1331.067442 -748.458257 -7850.951052 406.958778 4077.888331
## 502 503 504 505 506
## 5282.252383 414.767734 -168.119798 -7488.649188 341.533993
## 507 508 509 510 511
## -5280.470861 1613.618563 -1523.722646 -8383.657563 -806.464600
## 512 513 514 515 516
## -2881.493417 -791.174993 1125.816824 -9709.986849 -7956.416090
## 517 518 519 520 521
## 24111.503933 9571.332654 5594.973127 -5636.434447 2515.908154
## 522 523 524 525 526
## 16729.545161 11134.885228 -24516.408200 -5346.980157 -4002.876840
## 527 528 529 530 531
## 4314.777261 -628.003085 -11372.202398 4155.782024 13659.063783
## 532 533 534 535 536
## -5269.977918 4097.035851 5265.583118 -2095.102830 -4837.323147
## 537 538 539 540 541
## -7353.899124 -2356.908300 8073.070480 -147.306676 -8414.947524
## 542 543 544 545 546
## 1568.278774 -853.087941 114.476918 -11283.995808 -11282.189775
## 547 548 549 550 551
## 1849.948495 6801.201092 -1545.282294 610.544733 -7952.230066
## 552 553 554 555 556
## 8353.924780 666.354931 -12189.161294 8950.730667 8413.910748
## 557 558 559 560 561
## -173.400524 4580.028765 -3862.114115 13832.458054 21180.388202
## 562 563 564 565 566
## -6846.509269 -10032.798639 6459.007140 -105.943028 3127.493136
## 567 568 569 570 571
## -7709.192734 -17610.745291 6418.343763 6171.953114 1622.735596
## 572 573 574 575 576
## 2816.345578 1482.201162 -2454.204223 14438.008579 -9969.680719
## 577 578 579 580 581
## -6532.223243 8444.752268 2567.383884 -6841.829504 7235.605354
## 582 583 584 585 586
## -4093.176787 -3054.039403 15435.010058 -14811.520782 8162.583544
## 587 588 589 590 591
## -218.262554 -6497.519929 -1017.224484 -6.610893 -10910.702971
## 592 593 594 595 596
## 1572.111773 -7375.236304 2856.970508 8644.424235 -7750.108389
## 597 598 599 600 601
## 5623.603381 2488.543734 6601.252386 -3463.936232 5887.219611
## 602 603 604 605 606
## -8576.782767 2003.912186 1013.252593 2879.462311 1229.210594
## 607 608 609 610 611
## 129.641689 -6076.703187 7827.786310 -1452.213050 -2835.958690
## 612 613 614 615 616
## -3704.839867 -8467.751397 11743.386300 4687.120286 -9575.712531
## 617 618 619 620 621
## 11393.540446 5785.759528 -5850.336826 26102.673561 -13148.065836
## 622 623 624 625 626
## -7056.874872 2904.052246 -4417.487086 -10835.954873 11082.672360
## 627 628 629 630 631
## -21879.231407 -2602.151697 8490.855142 10925.821397 -1791.691526
## 632 633 634 635 636
## 33050.460530 -6903.003361 5426.701323 5101.003078 -2572.730097
## 637 638 639 640 641
## -5635.448839 -2210.082896 -12691.262598 -2468.252415 -2105.990178
## 642 643 644 645 646
## -2735.380330 -3067.301475 1611.761238 4221.566484 16743.493615
## 647 648 649 650 651
## 18289.864148 577.644080 4487.850906 10305.689848 19826.851280
## 652 653 654 655 656
## 386.926595 -28406.388372 -1603.956644 -2543.094461 1628.596654
## 657 658 659 660 661
## -3429.831627 -10846.914881 1466.384836 4025.606436 -1215.367844
## 662 663 664 665 666
## 12827.288264 1129.606729 1583.291285 -11921.559616 1175.191994
## 667 668 669 670 671
## 982.011697 -5371.938391 -7603.935062 1888.320988 -3894.212969
## 672 673 674 675 676
## 2497.549674 -3560.850526 -9513.084940 -8468.393414 -3131.159071
## 677 678 679 680 681
## 18.193092 2687.035114 544.297966 -3999.602687 -1977.834130
## 682 683 684 685 686
## -1487.697784 -8412.730967 4488.284380 -2414.122765 -1569.179043
## 687 688 689 690 691
## 415.953763 10678.613778 9656.013831 10415.905885 -9882.551991
## 692 693 694 695 696
## -3753.282117 -3331.354636 5685.174951 -10579.041522 -8088.266555
## 697 698 699 700 701
## -8777.302639 -6430.946306 -4891.242406 2931.796253 -4560.556092
## 702 703 704 705 706
## -2054.770620 4064.392896 30940.748263 9341.537294 23271.771417
## 707 708 709 710 711
## 1517.603150 8167.866411 22773.115956 6426.326448 -18329.022438
## 712 713 714 715 716
## 4698.381151 -5563.943751 -222.046983 357.886616 -17388.972031
## 717 718 719 720 721
## -5392.626242 3204.746107 -3142.322207 -13108.050629 4146.446242
## 722 723 724 725 726
## -5687.730275 609.580228 -4066.976052 -12580.394026 1231.243820
## 727 728 729 730 731
## -2002.284434 -9912.715537 17131.479032 1643.509543 -2853.827471
## 732 733 734 735 736
## 5583.499849 -8760.362502 -855.601937 8006.231568 -15480.951297
## 737 738 739 740 741
## -6044.875637 7273.001447 -4917.055064 27.134639 1694.418263
## 742 743 744 745 746
## -2088.325622 -5300.844986 6278.635577 -6406.706372 22565.377797
## 747 748 749 750 751
## 7701.450627 -2070.256110 -7412.572126 23289.409194 -4409.343240
## 752 753 754 755 756
## 1276.446129 -14541.781603 55983.586111 26823.431953 15012.721992
## 757 758 759 760 761
## -10721.086718 10525.001608 7236.292748 5732.684160 -46465.515590
## 762 763 764 765 766
## -16274.375503 853.853101 -2629.016332 -3570.278372 122730.166802
## 767 768 769 770 771
## 19282.368748 43697.774369 22586.363392 12082.068231 15849.562545
## 772 773 774 775 776
## 25729.031444 -98800.809360 -6833.763713 -35916.342813 1624.392670
## 777 778 779 780 781
## -1338.990345 3286.057757 -7521.221949 -1556.111167 -2056.115598
## 782 783 784 785 786
## 3313.481132 -7262.184007 -2347.969376 3808.141343 2158.629910
## 787 788 789 790 791
## -2862.835427 -4152.210740 1632.232981 2749.411231 -152.173687
## 792 793 794 795 796
## -6803.435381 -5886.874800 -1254.961977 -1370.241362 -7924.030304
## 797 798 799 800 801
## -2465.292439 -3379.575775 -2777.669780 10612.040671 2138.813069
## 802 803 804 805 806
## 6979.758109 2829.361369 -5540.227169 8091.320925 9784.806476
## 807 808 809 810 811
## -10685.192323 -7488.111059 -7602.178832 2904.073740 4095.675084
## 812 813 814 815 816
## -2364.467205 -14261.134594 -4216.271401 6152.470991 8136.125233
## 817 818 819 820 821
## -9767.286384 -7850.514637 -9441.005162 9644.187979 -1323.976740
## 822 823 824 825 826
## -4472.656390 -8549.833511 7767.235560 7813.682776 4881.339690
## 827 828 829 830 831
## -3193.148579 -805.089038 2798.252426 4576.615306 1535.108370
## 832 833 834 835 836
## -6782.890712 1994.839699 -491.080276 2660.009901 4048.739336
## 837 838 839 840 841
## -1226.232023 -9425.985780 5579.200836 -4954.456252 -7673.726984
## 842 843 844 845 846
## 2922.153477 -13140.737850 -2923.139717 11523.977664 1215.278406
## 847 848 849 850 851
## 540.479882 -755.545930 4418.374839 12594.565835 -3773.698213
## 852 853 854 855 856
## -11654.797603 -1307.048404 5240.067818 7455.409187 -5924.649571
## 857 858 859 860 861
## -5760.776485 1482.510778 -2356.197722 2408.409395 -2384.230788
## 862 863 864 865 866
## -1910.242758 16391.249178 -2387.294306 9463.450118 15821.047813
## 867 868 869 870 871
## 91.715496 -2766.818511 5790.524468 -16976.902367 9014.794812
## 872 873 874 875 876
## 5492.917793 -10208.614591 8575.379529 7449.265378 -8744.241507
## 877 878 879 880
## 3784.161407 7033.136268 -3869.512968 25303.197455
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17253.96 20100.15 24352.67 24070.73 26423.28 23756.85 24472.93 19706.69
## 10 11 12 13 14 15 16 17
## 19443.34 16787.02 17564.21 14294.74 14345.89 15010.31 16706.30 15026.90
## 18 19 20 21 22 23 24 25
## 16061.21 15434.96 22514.96 21599.29 21079.32 22968.68 22294.90 22947.22
## 26 27 28 29 30 31 32 33
## 24792.54 18723.03 20448.58 28283.20 28340.83 28013.86 25644.09 27046.04
## 34 35 36 37 38 39 40 41
## 30887.75 31236.05 32644.74 30156.21 34135.45 37342.42 34399.54 31211.53
## 42 43 44 45 46 47 48 49
## 30059.71 20642.50 28158.63 30594.26 31683.09 38518.82 38013.58 42674.15
## 50 51 52 53 54 55 56 57
## 46908.95 39609.40 34179.73 29204.16 22350.97 28638.79 25220.61 21519.50
## 58 59 60 61 62 63 64 65
## 25926.33 27185.44 27482.35 27894.03 23766.39 40351.91 42195.23 37442.84
## 66 67 68 69 70 71 72 73
## 41648.33 46562.27 57226.73 55241.20 40489.51 37996.24 41013.09 35315.22
## 74 75 76 77 78 79 80 81
## 30769.04 21475.71 24676.08 20603.21 22688.54 17585.85 19600.58 18827.38
## 82 83 84 85 86 87 88 89
## 17855.44 15925.04 17202.37 20809.17 25225.36 26214.88 26239.83 26856.35
## 90 91 92 93 94 95 96 97
## 30979.99 29810.95 30809.96 28875.07 28075.17 28443.18 28849.62 22429.31
## 98 99 100 101 102 103 104 105
## 25443.30 18476.20 17332.73 15374.14 15673.79 16350.94 20822.02 19905.85
## 106 107 108 109 110 111 112 113
## 23415.72 23205.76 24881.29 27757.05 25256.04 21700.77 21976.09 24621.44
## 114 115 116 117 118 119 120 121
## 35482.13 33675.61 35512.45 38509.72 40464.94 38154.17 32977.01 29319.68
## 122 123 124 125 126 127 128 129
## 31401.78 29682.33 30863.48 38460.56 38103.15 37165.02 34029.66 35810.23
## 130 131 132 133 134 135 136 137
## 41206.21 40646.70 31851.50 33115.86 36304.49 32716.40 31104.25 30189.57
## 138 139 140 141 142 143 144 145
## 26747.82 28161.74 27931.45 25618.57 27642.28 26267.53 19871.92 22901.37
## 146 147 148 149 150 151 152 153
## 20728.83 23704.89 24244.69 25834.51 26016.53 27670.10 28970.38 32001.46
## 154 155 156 157 158 159 160 161
## 27473.24 26736.46 24292.59 30184.58 41768.12 40098.95 37464.84 42483.32
## 162 163 164 165 166 167 168 169
## 43891.39 47306.07 42773.62 38082.48 43505.78 59802.13 62013.46 60428.13
## 170 171 172 173 174 175 176 177
## 57254.96 55654.78 58356.39 57380.84 49675.72 52498.21 56239.49 56275.67
## 178 179 180 181 182 183 184 185
## 63401.94 53907.64 50659.75 41471.71 33014.10 36521.10 46620.94 46077.15
## 186 187 188 189 190 191 192 193
## 52027.77 57770.68 68549.79 73830.75 67527.71 67721.07 74788.84 70466.84
## 194 195 196 197 198 199 200 201
## 65991.28 55238.99 49271.27 50695.06 46291.35 38462.97 44885.40 43113.44
## 202 203 204 205 206 207 208 209
## 42751.54 43229.71 50021.28 58907.29 58537.03 60261.21 61916.41 65706.22
## 210 211 212 213 214 215 216 217
## 75169.33 67255.00 55448.32 50059.07 41058.66 38017.48 41130.02 31153.81
## 218 219 220 221 222 223 224 225
## 48121.19 55439.34 56340.91 79098.11 86590.41 88592.91 96181.52 87135.70
## 226 227 228 229 230 231 232 233
## 81136.30 80718.38 77369.56 76531.36 81266.66 82630.44 77067.92 72282.59
## 234 235 236 237 238 239 240 241
## 77934.19 64586.83 56626.22 48579.78 40194.39 44384.78 46536.73 39989.80
## 242 243 244 245 246 247 248 249
## 33671.66 43950.39 38199.37 42133.64 34400.33 33106.66 36760.62 39583.48
## 250 251 252 253 254 255 256 257
## 30416.28 36352.18 40152.22 45345.11 48062.44 47555.20 57852.39 75343.23
## 258 259 260 261 262 263 264 265
## 75158.35 68471.45 69952.74 66174.91 67620.52 61380.48 50659.00 46687.80
## 266 267 268 269 270 271 272 273
## 46897.14 43005.28 51806.21 48080.65 52225.88 50343.13 54409.02 54704.93
## 274 275 276 277 278 279 280 281
## 60720.17 58355.27 68024.72 61951.71 62161.67 60511.34 66252.13 59984.89
## 282 283 284 285 286 287 288 289
## 56549.99 46106.09 44503.41 61729.41 67257.50 67666.81 65085.33 64173.03
## 290 291 292 293 294 295 296 297
## 68172.55 72081.37 53084.79 43190.41 37203.61 47521.27 50761.92 49876.19
## 298 299 300 301 302 303 304 305
## 74063.79 80007.05 80674.47 85285.27 83484.48 78516.53 81982.11 56889.94
## 306 307 308 309 310 311 312 313
## 53152.40 52832.29 46624.68 43835.12 47437.18 39992.04 38714.59 33278.07
## 314 315 316 317 318 319 320 321
## 37061.27 36242.90 40073.01 38058.15 63835.33 61677.26 63300.27 71295.56
## 322 323 324 325 326 327 328 329
## 73682.05 99154.71 97532.94 73371.67 72169.94 70527.85 62482.40 59605.65
## 330 331 332 333 334 335 336 337
## 29564.61 33250.87 33690.25 36009.37 35350.43 41115.43 42190.65 37439.89
## 338 339 340 341 342 343 344 345
## 36654.58 36781.20 32101.79 38098.70 38761.87 39020.23 39895.74 41681.05
## 346 347 348 349 350 351 352 353
## 43502.41 43254.16 36201.23 26772.30 32115.14 30976.54 30566.73 28183.99
## 354 355 356 357 358 359 360 361
## 32861.28 36614.28 41076.38 39265.99 40525.85 42663.59 50056.33 50606.73
## 362 363 364 365 366 367 368 369
## 50808.33 53270.66 50754.94 50199.67 42847.34 40045.06 36223.32 34002.75
## 370 371 372 373 374 375 376 377
## 30061.86 37347.32 39633.51 47516.40 41490.05 40937.41 39475.20 39008.13
## 378 379 380 381 382 383 384 385
## 29878.85 34475.43 27529.42 35746.02 46075.65 49639.91 47913.86 49904.88
## 386 387 388 389 390 391 392 393
## 56122.97 65605.30 58818.84 53288.88 53017.67 60394.82 60918.03 69583.85
## 394 395 396 397 398 399 400 401
## 58693.09 60259.47 59819.72 59303.75 57791.14 56553.11 43316.03 51898.90
## 402 403 404 405 406 407 408 409
## 50899.65 49866.21 56265.44 48810.32 48121.69 46448.91 42127.53 40958.61
## 410 411 412 413 414 415 416 417
## 39023.10 33119.14 40989.43 43914.84 38588.84 33678.64 48545.60 52389.17
## 418 419 420 421 422 423 424 425
## 56317.21 48788.74 45113.20 43789.59 47374.93 35797.66 35526.96 29787.54
## 426 427 428 429 430 431 432 433
## 35375.63 43700.64 50590.64 47357.09 44426.69 41352.83 41241.05 37719.95
## 434 435 436 437 438 439 440 441
## 33859.53 31094.43 32670.03 34518.84 32524.07 37388.74 43592.36 40345.71
## 442 443 444 445 446 447 448 449
## 40051.80 43057.30 40944.02 44932.31 40180.67 31215.69 30053.50 41406.97
## 450 451 452 453 454 455 456 457
## 41088.60 46738.08 42375.54 42725.09 44345.10 48062.19 37939.51 42787.34
## 458 459 460 461 462 463 464 465
## 38211.97 45778.74 49297.72 51917.49 48648.02 50988.10 51189.57 52936.08
## 466 467 468 469 470 471 472 473
## 52433.83 55376.71 52705.79 57755.46 51017.41 48629.05 47215.63 43840.99
## 474 475 476 477 478 479 480 481
## 47595.99 55057.10 49485.72 51188.45 45978.84 44358.33 47190.43 36607.80
## 482 483 484 485 486 487 488 489
## 30171.27 32040.04 34735.91 36225.44 37191.01 30833.70 43360.20 50018.41
## 490 491 492 493 494 495 496 497
## 56846.65 51559.70 56391.31 64022.70 67832.97 54093.40 44674.07 42699.64
## 498 499 500 501 502 503 504 505
## 43022.74 43813.67 38302.04 40700.25 46000.18 51680.09 52389.55 52500.08
## 506 507 508 509 510 511 512 513
## 46203.89 47543.47 43803.81 46558.44 46224.23 39941.89 41072.64 40248.03
## 514 515 516 517 518 519 520 521
## 41353.33 43992.56 36834.84 32115.64 55998.10 64156.31 67808.15 61189.23
## 522 523 524 525 526 527 528 529
## 62528.31 76109.83 83084.41 58042.27 52913.88 49609.22 53986.86 53493.35
## 530 531 532 533 534 535 536 537
## 43679.93 48670.22 61326.84 55849.39 59245.99 63232.53 60286.04 55318.33
## 538 539 540 541 542 543 544 545
## 48782.62 47438.93 55373.59 55124.09 47686.44 49909.37 49736.09 50429.71
## 546 547 548 549 550 551 552 553
## 41081.62 32919.91 37260.37 45374.43 45171.46 46876.80 40888.50 49898.65
## 554 555 556 557 558 559 560 561
## 51053.59 40835.98 50373.95 58234.26 57599.40 61195.97 56964.54 68721.33
## 562 563 564 565 566 567 568 569
## 85404.65 75498.80 64065.99 68483.80 66608.79 67795.05 59367.75 43361.94
## 570 571 572 573 574 575 576 577
## 50368.33 56271.55 57453.94 59528.80 60175.63 57302.99 69545.68 58922.51
## 578 579 580 581 582 583 584 585
## 52647.53 60246.62 61750.12 54846.39 61110.89 56688.47 53733.99 67299.66
## 586 587 588 589 590 591 592 593
## 52732.99 60074.83 59167.52 52891.80 52197.18 52473.13 43192.03 45987.95
## 594 595 596 597 598 599 600 601
## 40616.17 44860.58 53620.97 46954.40 52811.46 55188.46 60855.65 57015.07
## 602 603 604 605 606 607 608 609
## 61827.21 53398.66 55278.03 56054.11 58361.50 58935.36 58476.27 52655.64
## 610 611 612 613 614 615 616 617
## 59714.93 57775.67 54873.84 51581.04 44546.33 56052.74 59938.86 50877.32
## 618 619 620 621 622 623 624 625
## 61275.81 65459.34 58951.33 81171.35 66299.16 58631.09 60633.34 55988.24
## 626 627 628 629 630 631 632 633
## 46326.90 57030.66 37593.58 37453.86 47018.89 57497.98 55543.25 84262.43
## 634 635 636 637 638 639 640 641
## 74452.01 76652.00 78288.73 73016.88 65738.65 62374.12 50283.25 48652.13
## 642 643 644 645 646 647 648 649
## 47544.09 46026.87 44412.10 47088.00 51703.79 66669.42 81088.64 78213.01
## 650 651 652 653 654 655 656 657
## 79116.45 84985.86 98425.79 93186.25 63466.81 60919.52 57874.97 58859.26
## 658 659 660 661 662 663 664 665
## 55301.49 45717.62 48101.11 52417.37 51609.85 63167.54 63045.28 63334.70
## 666 667 668 669 670 671 672 673
## 51794.24 53153.27 54171.37 49511.79 43493.68 46527.50 44127.16 47612.71
## 674 675 676 677 678 679 680 681
## 45365.94 38206.11 32866.02 32863.52 35611.54 40341.84 42601.46 40606.69
## 682 683 684 685 686 687 688 689
## 40630.27 41078.87 35423.29 41750.41 41248.04 41547.19 43541.96 54245.84
## 690 691 692 693 694 695 696 697
## 62700.09 70746.41 60047.14 56056.35 52939.83 58092.04 48388.41 42089.73
## 698 699 700 701 702 703 704 705
## 35987.66 32707.96 31188.49 36693.13 34957.34 35629.75 41560.54 70209.61
## 706 707 708 709 710 711 712 713
## 76365.94 93906.68 90227.28 92821.60 107841.24 106682.31 84052.48 84399.66
## 714 715 716 717 718 719 720 721
## 75741.19 72844.97 70822.26 53558.34 48958.40 52449.18 49954.91 39074.13
## 722 723 724 725 726 727 728 729
## 44640.02 40912.71 43156.98 41032.97 31743.76 35693.00 36318.00 29955.95
## 730 731 732 733 734 735 736 737
## 48016.78 50263.54 48298.21 53949.93 46359.46 46633.91 54612.24 41069.02
## 738 739 740 741 742 743 744 745
## 37482.43 45980.34 42756.15 44258.15 47025.75 46139.27 42559.79 49545.85
## 746 747 748 749 750 751 752 753
## 44568.91 65522.84 70840.97 66951.86 58890.45 78661.49 71738.55 70658.21
## 754 755 756 757 758 759 760 761
## 55901.41 104601.71 121665.28 126252.37 107785.86 110213.14 109460.89 107490.94
## 762 763 764 765 766 767 768 769
## 60188.23 45245.43 47153.87 45778.99 43756.40 152282.92 156717.94 181911.78
## 770 771 772 773 774 775 776 777
## 185476.79 179417.01 177415.25 184294.52 81555.34 72148.49 38537.32 41968.85
## 778 779 780 781 782 783 784 785
## 42377.66 46773.51 41174.68 41494.54 41337.23 45888.90 40628.40 40326.00
## 786 787 788 789 790 791 792 793
## 45437.80 48461.26 46716.50 44066.91 46804.45 50170.60 50576.29 45122.30
## 794 795 796 797 798 799 800 801
## 41159.96 41744.67 42154.60 36789.44 36871.15 36144.10 36034.82 47632.04
## 802 803 804 805 806 807 808 809
## 50360.10 56969.78 59117.37 53683.96 60843.05 68573.62 57448.83 50525.89
## 810 811 812 813 814 815 816 817
## 44380.78 48189.18 52555.47 50726.99 38741.41 37046.67 44621.30 52968.14
## 818 819 820 821 822 823 824 825
## 44622.80 39009.01 32717.81 43890.26 44068.66 41474.83 35649.34 44811.17
## 826 827 828 829 830 831 832 833
## 52852.37 57313.72 54158.52 53488.60 56050.24 59840.18 60493.75 53800.73
## 834 835 836 837 838 839 840 841
## 55621.22 55040.13 57284.40 60456.95 58620.99 49857.23 55307.60 50868.58
## 842 843 844 845 846 847 848 849
## 44617.56 48411.74 37698.00 37264.74 49502.44 51186.95 52068.69 51706.91
## 850 851 852 853 854 855 856 857
## 55909.15 66718.70 61864.51 50743.33 50067.93 55195.45 61607.65 55522.92
## 858 859 860 861 862 863 864 865
## 50352.49 52162.63 50391.16 53004.95 51102.24 49854.61 64747.01 61353.41
## 866 867 868 869 870 871 872 873
## 68738.67 80739.71 77483.96 72144.62 74954.76 57526.92 65005.37 68460.47
## 874 875 876 877 878 879 880
## 57766.19 64830.59 70016.24 60403.55 62949.72 68010.37 62908.80
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.808
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 5.59238 0.7650541 4.056268
## t2* 2770.79947 159.7535373 889.477565
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.406698 5.533154 14.1174
## 2 lag_depvar 1701.098033 2802.816498 4588.0517Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | 9.4841 | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | 253.9948 | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | 57.1023 | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | 4.3670 | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | 0.0000 | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | 34.6850 | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | 18.2479 | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | 15.3980 | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | 0.0000 | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | 1.8275 | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 395.1066 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2869, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))## API key has been set for the current session.try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))## API key has been set for the current session.# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y)
# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
uf_timegpt,
h = 298, # 298 días a pronosticar
freq = "D", # Frecuencia diaria
add_history = TRUE, # Incluir datos históricos en el output
level = c(80,95),
model= "timegpt-1-long-horizon",
clean_ex_first = TRUE
)## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>%
mutate(ds = as.Date(ds))
timegpt_fcst <- timegpt_fcst %>%
mutate(ds = as.Date(ds))
# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
uf_timegpt %>% mutate(type = "Histórico"),
timegpt_fcst %>% mutate(type = "Pronóstico")
)
# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
# Intervalo de confianza del 95%
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
# Intervalo de confianza del 80%
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
# Línea histórica
geom_line(data = filter(full_data, type == "Histórico"),
aes(color = "Histórico"), size = 1) +
# Línea de pronóstico
geom_line(data = filter(full_data, type == "Pronóstico"),
aes(color = "Pronóstico"), size = 1) +
# Línea vertical separadora
geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds),
linetype = "dashed", color = "red", size = 0.8) +
# Configuración del eje x
scale_x_date(
date_breaks = "3 months", # Reduce la frecuencia de las etiquetas
date_labels = "%b %Y", # Formato de etiquetas (mes y año)
) +
# Configuración del eje y
scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
# Configuración de colores
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
# Títulos y subtítulos
labs(
title = "Pronóstico de Serie Temporal con TimeGPT",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Valor",
color = "Leyenda"
) +
# Tema y estilos
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)## Warning: Removed 2869 rows containing missing values or values outside the scale range
## (`geom_ribbon()`).
## Removed 2869 rows containing missing values or values outside the scale range
## (`geom_ribbon()`).## Warning: Removed 2869 rows containing missing values or values outside the scale range
## (`geom_line()`).
library(prophet)## Warning: package 'prophet' was built under R version 4.4.3## Loading required package: Rcpp## Warning: package 'Rcpp' was built under R version 4.4.3## Loading required package: rlang## Warning: package 'rlang' was built under R version 4.4.3##
## Attaching package: 'rlang'## The following objects are masked from 'package:purrr':
##
## flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
## flatten_raw, invoke, splice## The following object is masked from 'package:sparklyr':
##
## invoke## The following object is masked from 'package:data.table':
##
## := model <- prophet(
cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
# Trend flexibility
growth = "linear",
changepoint.prior.scale = 0.05, # Reduced for smoother trend
n.changepoints = 50, # Increased from default 25
# Seasonality
yearly.seasonality = TRUE,
weekly.seasonality = TRUE,
daily.seasonality = FALSE, # Disabled for daily data
seasonality.mode = "additive",
seasonality.prior.scale = 15, # Increased to capture stronger seasonality
# Holidays (if applicable)
# holidays = generated_holidays # Create with add_country_holidays()
# Uncertainty intervals
interval.width = 0.95,
uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistentforecast$ds <- as.Date(forecast$ds)
ggplot(forecast, aes(x = ds, y = yhat)) +
geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper),
fill = "#9ecae1", alpha = 0.4) +
geom_line(color = "#08519c", linewidth = 0.8) +
geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
scale_y_continuous(labels = scales::comma) +
labs(title = "Valores predichos (95%IC)",
# subtitle = "March 10, 2025 - May 7, 2025",
x = "Fecha",
y = "Valor",
# caption = "Source: Prophet Forecast Model"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(color = "gray50"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
plot.caption = element_text(color = "gray30")
)
La proyección de la UF a 298 días más 2025-11-09 00:04:58 sería de: 26.702 pesos// Percentil 95% más alto proyectado: 35.122,54
Según TimeGPT: La proyección de la UF a 298 días más 2026-09-03 sería de: 40.200,95 pesos// Percentil 80% más alto proyectado: 42.731,44 pesos// Percentil 95% más alto proyectado: 42.804,31
Según prophet: La proyección de la UF a 298 días más 2026-09-03 sería de: 42.605 pesos// Percentil 95% más alto proyectado: 50.686
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## i Please use tidy evaluation idioms with `aes()`.
## i See also `vignette("ggplot2-in-packages")` for more information.
## i The deprecated feature was likely used in the ggfortify package.
## Please report the issue at <https://github.com/sinhrks/ggfortify/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 26316.69 | 26321.15 |
| Lo.80 | 26449.27 | 26486.55 |
| Point.Forecast | 26701.54 | 26799.01 |
| Hi.80 | 31503.83 | 32192.83 |
| Hi.95 | 34386.37 | 35048.15 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.4448 1031.957
## s.e. 0.1012 38.334
##
## sigma^2 = 38228: log likelihood = -541.36
## AIC=1088.72 AICc=1089.03 BIC=1095.9paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.4524 582.0016 13.7474
## s.e. 0.1028 304.8464 9.2298
##
## sigma^2 = 37617: log likelihood = -540.2
## AIC=1088.39 AICc=1088.92 BIC=1097.97forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 628.5561 | 604.0737 | 614.9288 |
| Lo.80 | 776.0967 | 752.1732 | 708.2388 |
| Point.Forecast | 1054.8074 | 1031.9398 | 924.8718 |
| Hi.80 | 1333.5182 | 1311.7064 | 1244.2713 |
| Hi.95 | 1481.0587 | 1459.8058 | 1455.8453 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))
Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")
Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))
Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
mutate(value = Tami + Andrés) %>%
rename(ds = mes_ano, y = value) %>%
mutate(#ds= format(ds, "%Y-%m"),
unique_id = "1") %>% #it is only one series
select(unique_id, ds, y)
#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y) %>%
mutate(ds = ymd(ds)) %>% # Convert 'ds' to Date
mutate(month = month(ds), year = year(ds)) %>% # Extract month and year
group_by(month, year) %>% # Group by month and year
summarise(average_y = mean(y))%>% # Calculate average y
mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
ungroup()%>%
select(ds, uf=average_y)
Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |>
dplyr::left_join(uf_timegpt_my, by=c("ds"="ds"))
#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
Gastos_casa_mensual_2022_timegpt_ex,
h = 12,
freq = "M", # Monthly frequency
add_history = TRUE,
level = c(80, 95),
model = "timegpt-1",#"timegpt-1-long-horizon",
clean_ex_first = TRUE
)
# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
# Combine historical and forecast data
full_data_gastos <- bind_rows(
Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)
full_data_gastos |>
dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |>
# Visualize results
ggplot(aes(x = ds, y = y)) +
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
geom_line(aes(color = type), linewidth = 1.5) +
geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
linetype = "dashed", color = "red", linewidth = 0.8) +
scale_x_date(
date_breaks = "3 months",
date_labels = "%b %Y"
) +
scale_y_continuous(
name = "Gastos Totales",
labels = scales::comma,
breaks = pretty(full_data_gastos$y, n = 10),
expand = expansion(mult = c(0.05, 0.05))
) +
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
labs(
title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Gastos Totales",
color = "Leyenda"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 26100)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
## system code page: 65001
##
## time zone: UTC
## tzcode source: internal
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] prophet_1.0 rlang_1.1.6 Rcpp_1.1.0 scales_1.4.0
## [5] ggiraph_0.9.2 tidytext_0.4.3 DT_0.34.0 autoplotly_0.1.4
## [9] rvest_1.0.5 plotly_4.11.0 xts_0.14.1 forecast_8.24.0
## [13] wordcloud_2.6 RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-16
## [17] NLP_0.3-2 tsibble_1.1.6 lubridate_1.9.4 forcats_1.0.1
## [21] dplyr_1.1.4 purrr_1.2.0 tidyr_1.3.1 tibble_3.3.0
## [25] ggplot2_4.0.0 tidyverse_2.0.0 sjPlot_2.9.0 lattice_0.22-6
## [29] gridExtra_2.3 plotrix_3.8-4 sparklyr_1.9.2 httr_1.4.7
## [33] readxl_1.4.5 zoo_1.8-14 stringr_1.6.0 stringi_1.8.7
## [37] DataExplorer_0.8.4 data.table_1.17.8 reshape2_1.4.4 fUnitRoots_4040.81
## [41] plyr_1.8.9 readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] rstudioapi_0.17.1 jsonlite_2.0.0 magrittr_2.0.4
## [4] farver_2.1.2 rmarkdown_2.30 vctrs_0.6.5
## [7] askpass_1.2.1 janitor_2.2.1 htmltools_0.5.8.1
## [10] curl_7.0.0 janeaustenr_1.0.0 cellranger_1.1.0
## [13] Formula_1.2-5 TTR_0.24.4 StanHeaders_2.32.10
## [16] parallelly_1.45.1 sass_0.4.10 KernSmooth_2.23-22
## [19] bslib_0.9.0 htmlwidgets_1.6.4 tokenizers_0.3.0
## [22] extraDistr_1.10.0 httr2_1.2.1 cachem_1.1.0
## [25] networkD3_0.4.1 igraph_2.2.1 lifecycle_1.0.4
## [28] pkgconfig_2.0.3 Matrix_1.7-0 R6_2.6.1
## [31] fastmap_1.2.0 future_1.67.0 snakecase_0.11.1
## [34] selectr_0.4-2 digest_0.6.37 colorspace_2.1-2
## [37] spatial_7.3-17 crosstalk_1.2.2 labeling_0.4.3
## [40] timechange_0.3.0 abind_1.4-8 compiler_4.4.0
## [43] bit64_4.6.0-1 fontquiver_0.2.1 withr_3.0.2
## [46] inline_0.3.21 S7_0.2.0 tseries_0.10-58
## [49] carData_3.0-5 DBI_1.2.3 QuickJSR_1.8.1
## [52] pkgbuild_1.4.8 gplots_3.2.0 openssl_2.3.4
## [55] rappdirs_0.3.3 loo_2.8.0 fBasics_4041.97
## [58] gtools_3.9.5 caTools_1.18.3 tools_4.4.0
## [61] lmtest_0.9-40 quantmod_0.4.28 future.apply_1.20.0
## [64] nnet_7.3-19 glue_1.8.0 quadprog_1.5-8
## [67] nlme_3.1-164 nixtlar_0.6.2 generics_0.1.4
## [70] gtable_0.3.6 tzdb_0.5.0 hms_1.1.4
## [73] xml2_1.4.1 car_3.1-3 pillar_1.11.1
## [76] vroom_1.6.6 bit_4.6.0 tidyselect_1.2.1
## [79] fontLiberation_0.1.0 its.analysis_1.6.0 knitr_1.50
## [82] fontBitstreamVera_0.1.1 urca_1.3-4 stats4_4.4.0
## [85] xfun_0.54 matrixStats_1.5.0 timeDate_4051.111
## [88] rstan_2.32.7 lazyeval_0.2.2 yaml_2.3.10
## [91] boot_1.3-30 evaluate_1.0.5 codetools_0.2-20
## [94] timeSeries_4041.111 data.tree_1.2.0 gdtools_0.4.4
## [97] cli_3.6.5 RcppParallel_5.1.11-1 systemfonts_1.3.1
## [100] jquerylib_0.1.4 globals_0.18.0 dbplyr_2.5.1
## [103] anytime_0.3.12 parallel_4.4.0 ggfortify_0.4.19
## [106] ellipsis_0.3.2 fracdiff_1.5-3 bitops_1.0-9
## [109] listenv_0.10.0 viridisLite_0.4.2 slam_0.1-55
## [112] crayon_1.5.3#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))